Overview

Dataset statistics

Number of variables37
Number of observations1460
Missing cells1571
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory244.0 KiB
Average record size in memory171.1 B

Variable types

Numeric17
Categorical19
DateTime1

Alerts

YearBuilt is highly correlated with YearRemodAdd and 4 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuilt and 2 other fieldsHigh correlation
GrLivArea is highly correlated with 2ndFlrSF and 5 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 2 other fieldsHigh correlation
FullBath is highly correlated with YearBuilt and 4 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with GrLivArea and 1 other fieldsHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
GarageCars is highly correlated with YearBuilt and 4 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 5 other fieldsHigh correlation
SalePrice is highly correlated with YearBuilt and 7 other fieldsHigh correlation
YearBuilt is highly correlated with YearRemodAdd and 3 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuilt and 2 other fieldsHigh correlation
GrLivArea is highly correlated with 1stFlrSF and 5 other fieldsHigh correlation
1stFlrSF is highly correlated with GrLivArea and 2 other fieldsHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 2 other fieldsHigh correlation
FullBath is highly correlated with GrLivArea and 2 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with GrLivArea and 1 other fieldsHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 2 other fieldsHigh correlation
GarageCars is highly correlated with YearBuilt and 2 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 6 other fieldsHigh correlation
SalePrice is highly correlated with YearBuilt and 7 other fieldsHigh correlation
YearBuilt is highly correlated with YearRemodAdd and 1 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuiltHigh correlation
GrLivArea is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSFHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 1 other fieldsHigh correlation
FullBath is highly correlated with GrLivArea and 2 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSFHigh correlation
GarageCars is highly correlated with OverallQual and 1 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 3 other fieldsHigh correlation
SalePrice is highly correlated with GrLivArea and 3 other fieldsHigh correlation
CentralAir is highly correlated with PoolQCHigh correlation
Heating is highly correlated with PoolQCHigh correlation
BldgType is highly correlated with PoolQCHigh correlation
Utilities is highly correlated with PoolQCHigh correlation
Foundation is highly correlated with PoolQCHigh correlation
SalePriceQCut is highly correlated with NeighborhoodHigh correlation
PoolQC is highly correlated with CentralAir and 8 other fieldsHigh correlation
Neighborhood is highly correlated with SalePriceQCut and 1 other fieldsHigh correlation
LandSlope is highly correlated with PoolQCHigh correlation
SaleCondition is highly correlated with PoolQCHigh correlation
BsmtCond is highly correlated with PoolQCHigh correlation
Id is highly correlated with PoolQCHigh correlation
LotArea is highly correlated with LandSlope and 1 other fieldsHigh correlation
LandSlope is highly correlated with LotArea and 2 other fieldsHigh correlation
Neighborhood is highly correlated with LandSlope and 19 other fieldsHigh correlation
BldgType is highly correlated with Neighborhood and 2 other fieldsHigh correlation
YearBuilt is highly correlated with Neighborhood and 15 other fieldsHigh correlation
YearRemodAdd is highly correlated with Neighborhood and 7 other fieldsHigh correlation
Foundation is highly correlated with Neighborhood and 7 other fieldsHigh correlation
RoofMatl is highly correlated with RoofStyle and 5 other fieldsHigh correlation
RoofStyle is highly correlated with LandSlope and 1 other fieldsHigh correlation
Exterior1st is highly correlated with Neighborhood and 6 other fieldsHigh correlation
ExterCond is highly correlated with OverallCondHigh correlation
GrLivArea is highly correlated with Neighborhood and 13 other fieldsHigh correlation
1stFlrSF is highly correlated with Neighborhood and 9 other fieldsHigh correlation
2ndFlrSF is highly correlated with Neighborhood and 8 other fieldsHigh correlation
FullBath is highly correlated with Neighborhood and 9 other fieldsHigh correlation
HalfBath is highly correlated with Neighborhood and 2 other fieldsHigh correlation
BedroomAbvGr is highly correlated with GrLivArea and 2 other fieldsHigh correlation
KitchenAbvGr is highly correlated with BldgTypeHigh correlation
KitchenQual is highly correlated with Neighborhood and 8 other fieldsHigh correlation
TotalBsmtSF is highly correlated with RoofMatl and 6 other fieldsHigh correlation
BsmtCond is highly correlated with OverallQual and 1 other fieldsHigh correlation
GarageType is highly correlated with Neighborhood and 2 other fieldsHigh correlation
GarageCars is highly correlated with Neighborhood and 7 other fieldsHigh correlation
PoolArea is highly correlated with RoofMatl and 3 other fieldsHigh correlation
PoolQC is highly correlated with Id and 20 other fieldsHigh correlation
Heating is highly correlated with Foundation and 1 other fieldsHigh correlation
HeatingQC is highly correlated with Neighborhood and 6 other fieldsHigh correlation
CentralAir is highly correlated with YearBuilt and 4 other fieldsHigh correlation
OverallQual is highly correlated with Neighborhood and 13 other fieldsHigh correlation
OverallCond is highly correlated with Neighborhood and 4 other fieldsHigh correlation
SaleType is highly correlated with PoolQC and 1 other fieldsHigh correlation
SaleCondition is highly correlated with PoolQC and 1 other fieldsHigh correlation
SalePrice is highly correlated with Neighborhood and 13 other fieldsHigh correlation
SalePriceQCut is highly correlated with Neighborhood and 9 other fieldsHigh correlation
BsmtCond has 37 (2.5%) missing values Missing
GarageType has 81 (5.5%) missing values Missing
PoolQC has 1453 (99.5%) missing values Missing
Id is uniformly distributed Uniform
Id has unique values Unique
2ndFlrSF has 829 (56.8%) zeros Zeros
HalfBath has 913 (62.5%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
GarageCars has 81 (5.5%) zeros Zeros
PoolArea has 1453 (99.5%) zeros Zeros

Reproduction

Analysis started2022-05-01 23:47:57.041162
Analysis finished2022-05-01 23:48:26.060850
Duration29.02 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:26.118697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.6100094
Coefficient of variation (CV)0.577152648
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2022-05-01T19:48:26.202248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9821
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9731
 
0.1%
Other values (1450)1450
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%

LotArea
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.82808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:26.292386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.264932
Coefficient of variation (CV)0.949075601
Kurtosis203.243271
Mean10516.82808
Median Absolute Deviation (MAD)1998
Skewness12.20768785
Sum15354569
Variance99625649.65
MonotonicityNot monotonic
2022-05-01T19:48:26.525456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.6%
600017
 
1.2%
900014
 
1.0%
840014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
91008
 
0.5%
81258
 
0.5%
Other values (1063)1317
90.2%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
14911
 
0.1%
15261
 
0.1%
15332
 
0.1%
15961
 
0.1%
168010
0.7%
18691
 
0.1%
18902
 
0.1%
19201
 
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%
638871
0.1%
572001
0.1%
535041
0.1%
532271
0.1%
531071
0.1%

LandSlope
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Gtl
1382 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl1382
94.7%
Mod65
 
4.5%
Sev13
 
0.9%

Length

2022-05-01T19:48:26.602283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:26.652299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
gtl1382
94.7%
mod65
 
4.5%
sev13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Neighborhood
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
100 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.494520548
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes225
15.4%
CollgCr150
 
10.3%
OldTown113
 
7.7%
Edwards100
 
6.8%
Somerst86
 
5.9%
Gilbert79
 
5.4%
NridgHt77
 
5.3%
Sawyer74
 
5.1%
NWAmes73
 
5.0%
SawyerW59
 
4.0%
Other values (15)424
29.0%

Length

2022-05-01T19:48:26.705328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names225
15.4%
collgcr150
 
10.3%
oldtown113
 
7.7%
edwards100
 
6.8%
somerst86
 
5.9%
gilbert79
 
5.4%
nridght77
 
5.3%
sawyer74
 
5.1%
nwames73
 
5.0%
sawyerw59
 
4.0%
Other values (15)424
29.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BldgType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1Fam
1220 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.299315068
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam1220
83.6%
TwnhsE114
 
7.8%
Duplex52
 
3.6%
Twnhs43
 
2.9%
2fmCon31
 
2.1%

Length

2022-05-01T19:48:26.777177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:26.827339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1fam1220
83.6%
twnhse114
 
7.8%
duplex52
 
3.6%
twnhs43
 
2.9%
2fmcon31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

YearBuilt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.267808
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:26.888421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.20290404
Coefficient of variation (CV)0.01532156307
Kurtosis-0.4395519416
Mean1971.267808
Median Absolute Deviation (MAD)25
Skewness-0.6134611725
Sum2878051
Variance912.2154126
MonotonicityNot monotonic
2022-05-01T19:48:26.975143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200667
 
4.6%
200564
 
4.4%
200454
 
3.7%
200749
 
3.4%
200345
 
3.1%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1035
70.9%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
 
0.3%
18821
 
0.1%
18852
 
0.1%
18902
 
0.1%
18922
 
0.1%
18931
 
0.1%
18981
 
0.1%
190010
0.7%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200749
3.4%
200667
4.6%
200564
4.4%
200454
3.7%
200345
3.1%
200223
 
1.6%
200120
 
1.4%

YearRemodAdd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.865753
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:27.072168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.64540681
Coefficient of variation (CV)0.01040141217
Kurtosis-1.272245192
Mean1984.865753
Median Absolute Deviation (MAD)13
Skewness-0.5035620027
Sum2897904
Variance426.2328223
MonotonicityNot monotonic
2022-05-01T19:48:27.155944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.2%
200697
 
6.6%
200776
 
5.2%
200573
 
5.0%
200462
 
4.2%
200055
 
3.8%
200351
 
3.5%
200248
 
3.3%
200840
 
2.7%
199636
 
2.5%
Other values (51)744
51.0%
ValueCountFrequency (%)
1950178
12.2%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
19559
 
0.6%
195610
 
0.7%
19579
 
0.6%
195815
 
1.0%
195918
 
1.2%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200840
2.7%
200776
5.2%
200697
6.6%
200573
5.0%
200462
4.2%
200351
3.5%
200248
3.3%
200121
 
1.4%

Utilities
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
AllPub
1459 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub1459
99.9%
NoSeWa1
 
0.1%

Length

2022-05-01T19:48:27.245476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:27.287030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
allpub1459
99.9%
nosewa1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Foundation
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.515753425
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc647
44.3%
CBlock634
43.4%
BrkTil146
 
10.0%
Slab24
 
1.6%
Stone6
 
0.4%
Wood3
 
0.2%

Length

2022-05-01T19:48:27.335775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:27.388590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
pconc647
44.3%
cblock634
43.4%
brktil146
 
10.0%
slab24
 
1.6%
stone6
 
0.4%
wood3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RoofMatl
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
CompShg
1434 
Tar&Grv
 
11
WdShngl
 
6
WdShake
 
5
ClyTile
 
1
Other values (3)
 
3

Length

Max length7
Median length7
Mean length6.996575342
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg1434
98.2%
Tar&Grv11
 
0.8%
WdShngl6
 
0.4%
WdShake5
 
0.3%
ClyTile1
 
0.1%
Membran1
 
0.1%
Metal1
 
0.1%
Roll1
 
0.1%

Length

2022-05-01T19:48:27.446793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:27.497054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
compshg1434
98.2%
tar&grv11
 
0.8%
wdshngl6
 
0.4%
wdshake5
 
0.3%
clytile1
 
0.1%
membran1
 
0.1%
metal1
 
0.1%
roll1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RoofStyle
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.62260274
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable1141
78.2%
Hip286
 
19.6%
Flat13
 
0.9%
Gambrel11
 
0.8%
Mansard7
 
0.5%
Shed2
 
0.1%

Length

2022-05-01T19:48:27.561059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:27.613969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
gable1141
78.2%
hip286
 
19.6%
flat13
 
0.9%
gambrel11
 
0.8%
mansard7
 
0.5%
shed2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Exterior1st
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
VinylSd
515 
HdBoard
222 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.979452055
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd515
35.3%
HdBoard222
15.2%
MetalSd220
15.1%
Wd Sdng206
 
14.1%
Plywood108
 
7.4%
CemntBd61
 
4.2%
BrkFace50
 
3.4%
WdShing26
 
1.8%
Stucco25
 
1.7%
AsbShng20
 
1.4%
Other values (5)7
 
0.5%

Length

2022-05-01T19:48:27.673929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd515
30.9%
hdboard222
13.3%
metalsd220
13.2%
wd206
 
12.4%
sdng206
 
12.4%
plywood108
 
6.5%
cemntbd61
 
3.7%
brkface50
 
3.0%
wdshing26
 
1.6%
stucco25
 
1.5%
Other values (6)27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ExterCond
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
TA
1282 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1282
87.8%
Gd146
 
10.0%
Fa28
 
1.9%
Ex3
 
0.2%
Po1
 
0.1%

Length

2022-05-01T19:48:27.748020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:27.792635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ta1282
87.8%
gd146
 
10.0%
fa28
 
1.9%
ex3
 
0.2%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GrLivArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.463699
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:27.851262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.4803834
Coefficient of variation (CV)0.3467456092
Kurtosis4.895120581
Mean1515.463699
Median Absolute Deviation (MAD)326
Skewness1.366560356
Sum2212577
Variance276129.6334
MonotonicityNot monotonic
2022-05-01T19:48:27.937246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
145610
 
0.7%
84810
 
0.7%
12009
 
0.6%
9129
 
0.6%
8168
 
0.5%
10928
 
0.5%
17287
 
0.5%
Other values (851)1352
92.6%
ValueCountFrequency (%)
3341
 
0.1%
4381
 
0.1%
4801
 
0.1%
5201
 
0.1%
6051
 
0.1%
6161
 
0.1%
6306
0.4%
6722
 
0.1%
6911
 
0.1%
6931
 
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%
36081
0.1%
34931
0.1%
34471
0.1%
33951
0.1%
32791
0.1%

1stFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.626712
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:28.033329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.587738
Coefficient of variation (CV)0.3325123481
Kurtosis5.745841482
Mean1162.626712
Median Absolute Deviation (MAD)234.5
Skewness1.376756622
Sum1697435
Variance149450.0792
MonotonicityNot monotonic
2022-05-01T19:48:28.269764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
89412
 
0.8%
84812
 
0.8%
67211
 
0.8%
6309
 
0.6%
8169
 
0.6%
4837
 
0.5%
9607
 
0.5%
Other values (743)1338
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
4951
 
0.1%
5205
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%
25241
0.1%
25151
0.1%
24441
0.1%
24111
0.1%
24021
0.1%

2ndFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.9924658
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:28.364233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.5284359
Coefficient of variation (CV)1.258034335
Kurtosis-0.5534635576
Mean346.9924658
Median Absolute Deviation (MAD)0
Skewness0.8130298163
Sum506609
Variance190557.0753
MonotonicityNot monotonic
2022-05-01T19:48:28.451212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0829
56.8%
72810
 
0.7%
5049
 
0.6%
5468
 
0.5%
6728
 
0.5%
6007
 
0.5%
7207
 
0.5%
8966
 
0.4%
8625
 
0.3%
7805
 
0.3%
Other values (407)566
38.8%
ValueCountFrequency (%)
0829
56.8%
1101
 
0.1%
1671
 
0.1%
1921
 
0.1%
2081
 
0.1%
2131
 
0.1%
2201
 
0.1%
2241
 
0.1%
2402
 
0.1%
2522
 
0.1%
ValueCountFrequency (%)
20651
0.1%
18721
0.1%
18181
0.1%
17961
0.1%
16111
0.1%
15891
0.1%
15401
0.1%
15381
0.1%
15231
0.1%
15191
0.1%

FullBath
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.565068493
Minimum0
Maximum3
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:28.529906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5509158013
Coefficient of variation (CV)0.3520074704
Kurtosis-0.8570428213
Mean1.565068493
Median Absolute Deviation (MAD)0
Skewness0.0365615584
Sum2285
Variance0.3035082201
MonotonicityNot monotonic
2022-05-01T19:48:28.592957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%
ValueCountFrequency (%)
09
 
0.6%
1650
44.5%
2768
52.6%
333
 
2.3%
ValueCountFrequency (%)
333
 
2.3%
2768
52.6%
1650
44.5%
09
 
0.6%

HalfBath
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3828767123
Minimum0
Maximum2
Zeros913
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:28.660249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5028853811
Coefficient of variation (CV)1.313439457
Kurtosis-1.076927284
Mean0.3828767123
Median Absolute Deviation (MAD)0
Skewness0.6758974482
Sum559
Variance0.2528937065
MonotonicityNot monotonic
2022-05-01T19:48:28.715469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%
ValueCountFrequency (%)
212
 
0.8%
1535
36.6%
0913
62.5%

BedroomAbvGr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866438356
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:28.778139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8157780441
Coefficient of variation (CV)0.2845964025
Kurtosis2.230874582
Mean2.866438356
Median Absolute Deviation (MAD)0
Skewness0.2117900963
Sum4185
Variance0.6654938173
MonotonicityNot monotonic
2022-05-01T19:48:28.840605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3804
55.1%
2358
24.5%
4213
 
14.6%
150
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
150
 
3.4%
2358
24.5%
3804
55.1%
4213
 
14.6%
521
 
1.4%
67
 
0.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4213
 
14.6%
3804
55.1%
2358
24.5%
150
 
3.4%
06
 
0.4%

KitchenAbvGr
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.046575342
Minimum0
Maximum3
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:28.908475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2203381984
Coefficient of variation (CV)0.2105325718
Kurtosis21.53240384
Mean1.046575342
Median Absolute Deviation (MAD)0
Skewness4.488396777
Sum1528
Variance0.04854892167
MonotonicityNot monotonic
2022-05-01T19:48:28.960398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
11392
95.3%
265
 
4.5%
32
 
0.1%
ValueCountFrequency (%)
32
 
0.1%
265
 
4.5%
11392
95.3%
01
 
0.1%

KitchenQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA735
50.3%
Gd586
40.1%
Ex100
 
6.8%
Fa39
 
2.7%

Length

2022-05-01T19:48:29.029803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:29.073120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ta735
50.3%
gd586
40.1%
ex100
 
6.8%
fa39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalBsmtSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.429452
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:29.131530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.7053245
Coefficient of variation (CV)0.4148790481
Kurtosis13.25048328
Mean1057.429452
Median Absolute Deviation (MAD)234.5
Skewness1.524254549
Sum1543847
Variance192462.3617
MonotonicityNot monotonic
2022-05-01T19:48:29.220990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
76812
 
0.8%
72812
 
0.8%
89411
 
0.8%
78011
 
0.8%
Other values (711)1283
87.9%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
2901
 
0.1%
3191
 
0.1%
3601
 
0.1%
3721
 
0.1%
3847
 
0.5%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
23961
0.1%
23921
0.1%

BsmtCond
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size1.9 KiB
TA
1311 
Gd
 
65
Fa
 
45
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA1311
89.8%
Gd65
 
4.5%
Fa45
 
3.1%
Po2
 
0.1%
(Missing)37
 
2.5%

Length

2022-05-01T19:48:29.299875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:29.343067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ta1311
92.1%
gd65
 
4.6%
fa45
 
3.2%
po2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageType
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size2.1 KiB
Attchd
870 
Detchd
387 
BuiltIn
88 
Basment
 
19
CarPort
 
9

Length

Max length7
Median length6
Mean length6.084118927
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd870
59.6%
Detchd387
26.5%
BuiltIn88
 
6.0%
Basment19
 
1.3%
CarPort9
 
0.6%
2Types6
 
0.4%
(Missing)81
 
5.5%

Length

2022-05-01T19:48:29.390700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:29.443062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
attchd870
63.1%
detchd387
28.1%
builtin88
 
6.4%
basment19
 
1.4%
carport9
 
0.7%
2types6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageCars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.767123288
Minimum0
Maximum4
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:29.501505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7473150101
Coefficient of variation (CV)0.4228991918
Kurtosis0.220997764
Mean1.767123288
Median Absolute Deviation (MAD)0
Skewness-0.3425489297
Sum2580
Variance0.5584797243
MonotonicityNot monotonic
2022-05-01T19:48:29.564953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%
ValueCountFrequency (%)
081
 
5.5%
1369
25.3%
2824
56.4%
3181
 
12.4%
45
 
0.3%
ValueCountFrequency (%)
45
 
0.3%
3181
 
12.4%
2824
56.4%
1369
25.3%
081
 
5.5%

PoolArea
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75890411
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:29.626600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.17730694
Coefficient of variation (CV)14.56277759
Kurtosis223.2684989
Mean2.75890411
Median Absolute Deviation (MAD)0
Skewness14.82837364
Sum4028
Variance1614.215993
MonotonicityNot monotonic
2022-05-01T19:48:29.681194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01453
99.5%
5121
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
4801
 
0.1%
5191
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
01453
99.5%
4801
 
0.1%
5121
 
0.1%
5191
 
0.1%
5551
 
0.1%
5761
 
0.1%
6481
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
7381
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
5191
 
0.1%
5121
 
0.1%
4801
 
0.1%
01453
99.5%

PoolQC
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)42.9%
Missing1453
Missing (%)99.5%
Memory size1.8 KiB
Gd
Ex
Fa

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd

Common Values

ValueCountFrequency (%)
Gd3
 
0.2%
Ex2
 
0.1%
Fa2
 
0.1%
(Missing)1453
99.5%

Length

2022-05-01T19:48:29.750433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:29.793487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
gd3
42.9%
ex2
28.6%
fa2
28.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Heating
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
GasA
1428 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.000684932
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA1428
97.8%
GasW18
 
1.2%
Grav7
 
0.5%
Wall4
 
0.3%
OthW2
 
0.1%
Floor1
 
0.1%

Length

2022-05-01T19:48:29.839652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:29.885873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
gasa1428
97.8%
gasw18
 
1.2%
grav7
 
0.5%
wall4
 
0.3%
othw2
 
0.1%
floor1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HeatingQC
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Ex
741 
TA
428 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex741
50.8%
TA428
29.3%
Gd241
 
16.5%
Fa49
 
3.4%
Po1
 
0.1%

Length

2022-05-01T19:48:30.107299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:30.156871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ex741
50.8%
ta428
29.3%
gd241
 
16.5%
fa49
 
3.4%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CentralAir
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Y
1365 
N
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y1365
93.5%
N95
 
6.5%

Length

2022-05-01T19:48:30.206788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:30.256172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
y1365
93.5%
n95
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

OverallQual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.099315068
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:30.292916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382996547
Coefficient of variation (CV)0.2267462053
Kurtosis0.09629277836
Mean6.099315068
Median Absolute Deviation (MAD)1
Skewness0.2169439278
Sum8905
Variance1.912679448
MonotonicityNot monotonic
2022-05-01T19:48:30.348945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
4116
 
7.9%
943
 
2.9%
320
 
1.4%
1018
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
7.9%
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
943
 
2.9%
1018
 
1.2%
ValueCountFrequency (%)
1018
 
1.2%
943
 
2.9%
8168
11.5%
7319
21.8%
6374
25.6%
5397
27.2%
4116
 
7.9%
320
 
1.4%
23
 
0.2%
12
 
0.1%

OverallCond
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.575342466
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:30.411096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112799337
Coefficient of variation (CV)0.1995930014
Kurtosis1.106413461
Mean5.575342466
Median Absolute Deviation (MAD)0
Skewness0.6930674725
Sum8140
Variance1.238322364
MonotonicityNot monotonic
2022-05-01T19:48:30.474619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
457
 
3.9%
325
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
325
 
1.7%
457
 
3.9%
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
922
 
1.5%
ValueCountFrequency (%)
922
 
1.5%
872
 
4.9%
7205
 
14.0%
6252
 
17.3%
5821
56.2%
457
 
3.9%
325
 
1.7%
25
 
0.3%
11
 
0.1%
Distinct671
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Minimum1872-01-12 00:00:00
Maximum2010-01-06 00:00:00
2022-05-01T19:48:30.567959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:30.671705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SaleType
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
WD
1267 
New
 
122
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.158219178
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD1267
86.8%
New122
 
8.4%
COD43
 
2.9%
ConLD9
 
0.6%
ConLI5
 
0.3%
ConLw5
 
0.3%
CWD4
 
0.3%
Oth3
 
0.2%
Con2
 
0.1%

Length

2022-05-01T19:48:30.754487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:30.806096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
wd1267
86.8%
new122
 
8.4%
cod43
 
2.9%
conld9
 
0.6%
conli5
 
0.3%
conlw5
 
0.3%
cwd4
 
0.3%
oth3
 
0.2%
con2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SaleCondition
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Normal
1198 
Partial
125 
Abnorml
 
101
Family
 
20
Alloca
 
12

Length

Max length7
Median length6
Mean length6.157534247
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal1198
82.1%
Partial125
 
8.6%
Abnorml101
 
6.9%
Family20
 
1.4%
Alloca12
 
0.8%
AdjLand4
 
0.3%

Length

2022-05-01T19:48:30.869987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:30.916430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal1198
82.1%
partial125
 
8.6%
abnorml101
 
6.9%
family20
 
1.4%
alloca12
 
0.8%
adjland4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SalePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-05-01T19:48:30.985113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotonicityNot monotonic
2022-05-01T19:48:31.077008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
15500014
 
1.0%
14500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
11500012
 
0.8%
16000012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (653)1323
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
520001
0.1%
525001
0.1%
550002
0.1%
559931
0.1%
585001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%
5565811
0.1%
5550001
0.1%
5380001
0.1%
5018371
0.1%
4850001
0.1%

SalePriceQCut
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Low-Mid
367 
Mid-High
366 
Low
365 
High
362 

Length

Max length8
Median length7
Mean length5.506849315
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid-High
2nd rowMid-High
3rd rowHigh
4th rowLow-Mid
5th rowHigh

Common Values

ValueCountFrequency (%)
Low-Mid367
25.1%
Mid-High366
25.1%
Low365
25.0%
High362
24.8%

Length

2022-05-01T19:48:31.158470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-01T19:48:31.204226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
low-mid367
25.1%
mid-high366
25.1%
low365
25.0%
high362
24.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-01T19:48:23.619942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:00.500730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:01.958255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:03.439126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:04.747970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:06.298980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:07.758188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:09.393146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:10.730954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:12.251352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:13.610259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:15.117731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-01T19:48:10.323591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:11.836413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:13.189853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:14.713382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:15.996395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:17.515190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:19.035094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:20.432512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:21.856386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:23.232325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:24.795705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:01.607689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:03.143584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:04.438717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:05.979750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:07.428824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:09.102484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:10.413377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:11.916709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:13.278020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:14.791641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:16.075014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:17.592746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:19.135231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:20.515060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:21.940286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:23.315079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:24.871613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:01.699889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:03.219285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:04.517773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:06.057063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:07.507207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:09.177962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:10.497302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:12.000167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:13.358003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:14.880641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:16.354207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:17.667858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:19.224724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:20.593495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:22.030973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:23.391856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:24.948965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:01.778927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:03.285989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:04.587782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:06.127803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:07.585023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:09.243660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:10.572030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:12.075648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:13.439763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:14.954738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:16.424989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:17.747835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:19.315398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:20.669101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:22.112801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:23.465389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:25.025126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:01.862791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:03.363366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:04.665283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:06.208946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:07.675155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:09.317221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:10.651276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:12.161653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:13.523669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:15.037316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:16.503813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:17.836477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:19.414763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:20.765621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:22.195354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-01T19:48:23.543132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-01T19:48:31.257187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-01T19:48:31.379840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-01T19:48:31.503009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-01T19:48:31.624775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-01T19:48:31.915449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-01T19:48:25.197106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-01T19:48:25.727511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-01T19:48:25.867594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-01T19:48:25.947193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdLotAreaLandSlopeNeighborhoodBldgTypeYearBuiltYearRemodAddUtilitiesFoundationRoofMatlRoofStyleExterior1stExterCondGrLivArea1stFlrSF2ndFlrSFFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotalBsmtSFBsmtCondGarageTypeGarageCarsPoolAreaPoolQCHeatingHeatingQCCentralAirOverallQualOverallCondDateSoldSaleTypeSaleConditionSalePriceSalePriceQCut
018450GtlCollgCr1Fam20032003AllPubPConcCompShgGableVinylSdTA17108568542131Gd856TAAttchd20NaNGasAExY752003-01-02WDNormal208500Mid-High
129600GtlVeenker1Fam19761976AllPubCBlockCompShgGableMetalSdTA1262126202031TA1262TAAttchd20NaNGasAExY681976-01-05WDNormal181500Mid-High
2311250GtlCollgCr1Fam20012002AllPubPConcCompShgGableVinylSdTA17869208662131Gd920TAAttchd20NaNGasAExY752001-01-09WDNormal223500High
349550GtlCrawfor1Fam19151970AllPubBrkTilCompShgGableWd SdngTA17179617561031Gd756GdDetchd30NaNGasAGdY751915-01-02WDAbnorml140000Low-Mid
4514260GtlNoRidge1Fam20002000AllPubPConcCompShgGableVinylSdTA2198114510532141Gd1145TAAttchd30NaNGasAExY852000-01-12WDNormal250000High
5614115GtlMitchel1Fam19931995AllPubWoodCompShgGableVinylSdTA13627965661111TA796TAAttchd20NaNGasAExY551993-01-10WDNormal143000Low-Mid
6710084GtlSomerst1Fam20042005AllPubPConcCompShgGableVinylSdTA1694169402031Gd1686TAAttchd20NaNGasAExY852004-01-08WDNormal307000High
7810382GtlNWAmes1Fam19731973AllPubCBlockCompShgGableHdBoardTA209011079832131TA1107TAAttchd20NaNGasAExY761973-01-11WDNormal200000Mid-High
896120GtlOldTown1Fam19311950AllPubBrkTilCompShgGableBrkFaceTA177410227522022TA952TADetchd20NaNGasAGdY751931-01-04WDAbnorml129900Low
9107420GtlBrkSide2fmCon19391950AllPubBrkTilCompShgGableMetalSdTA1077107701022TA991TAAttchd10NaNGasAExY561939-01-01WDNormal118000Low

Last rows

IdLotAreaLandSlopeNeighborhoodBldgTypeYearBuiltYearRemodAddUtilitiesFoundationRoofMatlRoofStyleExterior1stExterCondGrLivArea1stFlrSF2ndFlrSFFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotalBsmtSFBsmtCondGarageTypeGarageCarsPoolAreaPoolQCHeatingHeatingQCCentralAirOverallQualOverallCondDateSoldSaleTypeSaleConditionSalePriceSalePriceQCut
145014519000GtlNAmesDuplex19741974AllPubCBlockCompShgGableVinylSdTA17928968962242TA896TANaN00NaNGasATAY551974-01-09WDNormal136000Low-Mid
145114529262GtlSomerst1Fam20082009AllPubPConcCompShgGableCemntBdTA1578157802031Ex1573TAAttchd30NaNGasAExY852008-01-05NewPartial287090High
145214533675GtlEdwardsTwnhsE20052005AllPubPConcCompShgGableVinylSdTA1072107201021TA547TABasment20NaNGasAGdY552005-01-05WDNormal145000Low-Mid
1453145417217GtlMitchel1Fam20062006AllPubPConcCompShgGableVinylSdTA1140114001031TA1140TANaN00NaNGasAExY552006-01-07WDAbnorml84500Low
145414557500GtlSomerst1Fam20042005AllPubPConcCompShgGableVinylSdTA1221122102021Gd1221TAAttchd20NaNGasAExY752004-01-10WDNormal185000Mid-High
145514567917GtlGilbert1Fam19992000AllPubPConcCompShgGableVinylSdTA16479536942131TA953TAAttchd20NaNGasAExY651999-01-08WDNormal175000Mid-High
1456145713175GtlNWAmes1Fam19781988AllPubCBlockCompShgGablePlywoodTA2073207302031TA1542TAAttchd20NaNGasATAY661978-01-02WDNormal210000Mid-High
145714589042GtlCrawfor1Fam19412006AllPubStoneCompShgGableCemntBdGd2340118811522041Gd1152GdAttchd10NaNGasAExY791941-01-05WDNormal266500High
145814599717GtlNAmes1Fam19501996AllPubCBlockCompShgHipMetalSdTA1078107801021Gd1078TAAttchd10NaNGasAGdY561950-01-04WDNormal142125Low-Mid
145914609937GtlEdwards1Fam19651965AllPubCBlockCompShgGableHdBoardTA1256125601131TA1256TAAttchd10NaNGasAGdY561965-01-06WDNormal147500Low-Mid